Peptihub (https://bioinformaticsscollege.ir/peptihub/) 是一个精心策划的癌症相关肽 (CRP) 存储库，这些肽已在科学文献中记录下来。 PeptiHub 中包含各种 CRP，展示了一系列效果和活动。虽然一些肽表现出显着的抗癌功效，但其他肽没有表现出明显的影响，有些甚至具有替代的非药物功能，包括药物载体或致癌属性。目前，Peptihub 拥有 874 个 CRP，对 10 个不同的生物类别、26 个器官和 438 个细胞系进行评估。数据库中的每个条目都附有易于访问的 3D 构象，这些构象是通过实验或通过预测方法获得的。为用户提供了三个搜索框架，提供基本、高级和 BLAST 序列搜索选项。此外，肽的精确注释使用户能够根据 CRP 的特定活性（抗癌、无作用、无显着作用、致癌物等）及其在癌症条件下（特别是在单个器官内）的有效性（速率和 IC50）来探索 CRP。这种独特的属性有助于构建强大的训练和测试数据集。此外，PeptiHub 还提供 1141 个功能，可以方便地选择最相关的功能来解决其特定的研究问题。功能包括aaindex1（六个主要子类别：α倾向、β倾向、成分指数、疏水性、理化性质和其他性质）、氨基酸组成（氨基酸组成和二肽组成）和分组氨基酸组成（分组氨基酸组成） 、分组二肽组合物和联合三联体）类别。这些实用程序不仅加速基于机器学习的肽设计，而且还促进肽分类。数据库网址：https://bioinformaticsscollege.ir/peptihub/。© 作者 2024。由牛津大学出版社出版。

Peptihub (https://bioinformaticscollege.ir/peptihub/) is a meticulously curated repository of cancer-related peptides (CRPs) that have been documented in scientific literature. A diverse collection of CRPs is included in the PeptiHub, showcasing a spectrum of effects and activities. While some peptides demonstrated significant anticancer efficacy, others exhibited no discernible impact, and some even possessed alternative non-drug functionalities, including drug carrier or carcinogenic attributes. Presently, Peptihub houses 874 CRPs, subjected to evaluation across 10 distinct organism categories, 26 organs, and 438 cell lines. Each entry in the database is accompanied by easily accessible 3D conformations, obtained either experimentally or through predictive methodology. Users are provided with three search frameworks offering basic, advanced, and BLAST sequence search options. Furthermore, precise annotations of peptides enable users to explore CRPs based on their specific activities (anticancer, no effect, insignificant effect, carcinogen, and others) and their effectiveness (rate and IC50) under cancer conditions, specifically within individual organs. This unique property facilitates the construction of robust training and testing datasets. Additionally, PeptiHub offers 1141 features with the convenience of selecting the most pertinent features to address their specific research questions. Features include aaindex1 (in six main subcategories: alpha propensities, beta propensity, composition indices, hydrophobicity, physicochemical properties, and other properties), amino acid composition (Amino acid Composition and Dipeptide Composition), and Grouped Amino Acid Composition (Grouped amino acid composition, Grouped dipeptide composition, and Conjoint triad) categories. These utilities not only speed up machine learning-based peptide design but also facilitate peptide classification. Database URL: https://bioinformaticscollege.ir/peptihub/.© The Author(s) 2024. Published by Oxford University Press.